DBMS > Cassandra vs. ObjectBox vs. Pinecone vs. Splice Machine vs. Yanza

System Properties Comparison Cassandra vs. ObjectBox vs. Pinecone vs. Splice Machine vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Extremely fast embedded database for small devices, IoT and Mobile

A managed, cloud-native vector database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	3.23
Rank	#92	Overall
	#2	Vector DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

yanza.com

Technical documentation

cassandra.apache.org/doc/latest

docs.objectbox.io

docs.pinecone.io/docs/overview

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

ObjectBox Limited

Pinecone Systems, Inc

Splice Machine

Yanza

Initial release

2008

2017

2019

2014

2015

Current release

4.1.3, July 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache License 2.0

commercial

Open Source

AGPL 3.0, commercial license available

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C and C++

Java

Server operating systems

BSD
Linux
OS X
Windows

Android
iOS
Linux
macOS
Windows

hosted

Linux
OS X
Solaris
Windows

Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

String, Number, Boolean

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

yes

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

Proprietary native API

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

HTTP API

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

online/offline synchronization between client and server

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users can be defined per object

yes

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
» more

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